Women experience high rates of depression, particularly during pregnancy and the postpartum periods. Using population-based data from Bangladesh and Pakistan, we estimated the burden of antenatal ...depression, its risk factors, and its effect on preterm birth.
The study uses the following data: maternal depression measured between 24 and 28 weeks of gestation using the 9-question Patient Health Questionnaire (PHQ-9); data on pregnancy including an ultrasound before 19 weeks of gestation; data on pregnancy outcomes; and data on woman's age, education, parity, weight, height, history of previous illness, prior miscarriage, stillbirth, husband's education, and household socioeconomic data collected during early pregnancy. Using PHQ-9 cutoff score of ≥12, women were categorized into none to mild depression or moderate to moderately severe depression. Using ultrasound data, preterm birth was defined as babies born <37 weeks of gestation. To identify risk ratios (RR) for antenatal depression, unadjusted and adjusted RR and 95% confidence intervals (CI) were calculated using log- binomial model. Log-binomial models were also used for determining the effect of antenatal depression on preterm birth adjusting for potential confounders. Data were analyzed using Stata version 16 (StataCorp LP).
About 6% of the women reported moderate to moderately severe depressive symptoms during the antenatal period. A parity of ≥2 and the highest household wealth status were associated with an increased risk of depression. The overall incidence of preterm birth was 13.4%. Maternal antenatal depression was significantly associated with the risk of preterm birth (ARR, 95% CI: 1.34, 1.02-1.74).
The increased risk of preterm birth in women with antenatal depression in conjunction with other significant risk factors suggests that depression likely occurs within a constellation of other risk factors. Thus, to effectively address the burden of preterm birth, programs require developing and providing integrated care addressing multiple risk factors.
Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a ...population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.
This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.
Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002).
Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.
Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of ...the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature.
A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes.
With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature.
In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs.
Differential diagnosis of the gangrenous tip of the nose as in the index case includes granulomatosis with polyangiitis,1 polyarteritis nodosa.2 Cold agglutinin disease presents during winters with ...skin lesions mimicking vasculitis,3 with M. pneumoniae being a known cause. If doxycycline is used, caution is recommended, especially in young children, owing to risk of teeth discolouration associated with use of tetracyclines.4 Short history, temporality with cold exposure and supportive investigations are pivotal in ascertaining diagnosis and avoiding unnecessary immunosuppression. Contributors KK: data collection and drafting of the initial manuscript; KK, TS: clinical data collection; HM, TS, HP: interpretation of lab results; NKB, AVR: concept, design and review of draft for the intellectual concept.
BACKGROUNDGlobally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for ...effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision.METHODSDried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard).RESULTSOverall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%.CONCLUSIONMetabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately.
Lignin nanoparticles synthesis is among recent developments in lignin valorization especially for biomedical applications. In this study, a new technique where complete self-assembling of lignin was ...ensured by simultaneous solvent displacement and flash pH change was used to optimize particle size of blank lignin nanoparticles (BLNPs) for suitability in cell uptake along with maximized yield. To establish BLNPs as drug carrier, safety studies including hemocompatibility, cytotoxicity and elaborate genotoxicity studies on Drosophila melanogaster as a model organism were done. Finally, irinotecan loaded lignin nanoparticles (DLNPs) were synthesized to establish their drug carrying potential and thorough in vitro characterization was performed. BLNPs with controllable size (⁓152 nm), low polydispersity (<0.2), maximized yield (>65%), negative surface charge (−22 to −23 mV), spherical shape and smooth surface were obtained with acceptable %hemolysis (<2%). In vitro cytotoxicity studies revealed that BLNPs were significantly toxic (74.38 ± 4.74%) in human breast adenocarcinoma (MCF-7), slightly toxic (38.8 ± 4.70%) in human alveolar epithelial adenocarcinoma (A-549) and insignificantly toxic (15.89 ± 2.84%) to human embryonic kidney (HEK-293) cells. BLNPs showed concentration dependent early neuronal defects in Drosophila, but nuclei fragmentation and gut cell damage were absent. Sustained release DLNPs with high drug loading reduced the IC50 value of irinotecan by almost 3 folds.
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•Complete self-assembling by simultaneous solvent displacement and pH change•BLNPs are safe on normal cells but cytotoxic on folate-receptor positive cells.•Exhibited concentration-dependent geno-compatibility in Drosophila melanogaster•Optimum payload and sustained release of drug was achieved.•Immense potential in nano-medical applications is suggested.
One common constraint in the practical application of speech recognition is Code Switching. The issue of code-switched languages is especially aggravated in the context of Indian languages – since ...most massively multilingual models are trained on corpora that are not representative of the diverse set of Indian languages. An associated constraint with such systems is the privacy-intrusive nature of the applications that aim to collate such representative data. To collectively mitigate both problems, this work presents CodeFed: A federated learning-based code-switching detection model that can be deployed to collaboratively be trained by leveraging private data from multiple users, without compromising their privacy. Using a representative low-resource Indic dataset, we demonstrate the superior performance of a collaboratively trained global model that is trained using federated learning on three low-resource Indic languages – Gujarati, Tamil and Telugu and draw a comparison of the model with respect to the most current work in the field. Finally, to evaluate the practical realizability of the proposed system, CodeFed also discusses the system overview of the label generation architecture which may accompany CodeFed’s possible real-time deployment.
Aim
To determine the clinicopathological characteristics and outcomes of children diagnosed with lupus nephritis in a tertiary hospital in western Rajasthan and compare it with the data available ...from other parts of India.
Material and methods:
A retrospective review of children presenting to a tertiary care center in western Rajasthan, India, with a diagnosis of pediatric Systemic Lupus Erythematosus (p SLE), between July 2017 and July 2020 was done. Comparisons of pediatric lupus in western India to other parts of country were done.
Results
19 children with SLE with Renal involvement were enrolled and followed up. The median age at presentation was 15 years (IQR-16–9.5) (73% females). 8/19 (42%) children presented with AKI, of which 62% children presented as rapidly progressive renal failure. Six (37.5%) patients required dialysis at presentation. 84.21% of children were evaluated with renal biopsy, 16 biopsies were done in 19 children, among which class II, III, and IV lupus nephritis were reported in 21%,42%, and 35% respectively(4 crescentic). Antiphospholipid antibodies were positive in 8/15(53%), children which is much higher than a reported incidence of 30% in other Indian studies. Ten patients (52%) had neurological involvement, with seizures being the most common form of presentation (60%). Seven patients (36%) developed hepatitis. We noted many uncommon presentations in the small group like Autoimmune Pancreatitis, Mononeuritis multiplex, and peripheral digital gangrene. Cyclophosphamide was used in 10 out of 19 patients for inducing remission with class 3 and 4 nephritis and MMF in 8 children. 55% patients attained remission (after completing induction), of which 4 relapsed during the follow up. Four patients were lost to follow-up. A total of 27% patients died and 10% patients developed end stage renal failure. It was seen that those who died had more cardiac and neurological involvement at presentation, higher grade of proteinuria, lower GFR, and need for dialysis at admission.
Conclusion:
We found a more severe form of clinical manifestation in pediatric SLE patients at the time of the first presentation in the form of severe renal and extrarenal manifestation compared to other parts of the country.
Segregation of waste using IOT Joshi, Harshita; Mittal, Amit
International journal of advanced research in computer science,
05/2017, Letnik:
8, Številka:
5
Journal Article
Recenzirano
Odprti dostop
In present scenario efficient waste management is the major objective of the country. Segregation of waste and creating awareness of different types of waste is new boom. Waste from the houses can be ...broadly divided into two categories dry waste and wet waste. It is recommended to have two separate dustbins in the house to keep wet waste from mixing up with its dry counterpart. Due to lack of awareness in citizens they prefer mix all the waste and disposed. It is just loss to ecosystem as well as individual health due to poisonous gas emission from waste. Bad smell spreads and may cause illness to human beings. It also leads to unhygienic environment and look of the city. In this proposed system smart dustbin will be created in which soil moisture sensor sense the moisture of waste and based on threshold value desired one will be enter as input. To achieve the overall scenario IOT technology will be used.
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